Network visualization using output from text model
Data preparation
edgelist <- read.csv("../../../Data/Text_Model_Data/edgelist.csv")
edgelist
Indicator title
indicator_info <- read.csv("../../../Data/Text_Model_Data/indicator_att.csv")
library(stringr)
str_replace_all(indicator_info$Indicator, fixed(" "), "")
Textdata <- datatable(indicator_info, rownames=TRUE, caption=htmltools::tags$caption(style="caption-side: bottom; text-align: center;", "Innovative counties in the U.S."), filter="top", extensions="Buttons", options=list(dom = "Bfrtip", buttons = c("colvis", "copy", "csv", "excel", "pdf", "print")))
Textdata
For future classification of indicators into the goals they belong to, create the nodes dataframe:
nodes <- edgelist %>%
select(indicator, related_indicator)
nodes <- data.frame(Indicator = unlist(nodes, use.names = FALSE))
nodes <- distinct(nodes)
str_replace_all(nodes$Indicator, fixed(" "), "")
[1] "1.1.1" "1.2.2" "1.3.1" "1.4.1" "1.4.2" "1.5.1" "1.5.2" "1.5.3" "1.5.4" "1.a.1" "1.a.2" "2.2.1"
[13] "2.2.2" "2.2.3" "2.3.1" "2.3.2" "2.4.1" "2.5.1" "2.5.2" "2.a.1" "2.a.2" "2.b.1" "2.c.1" "3.1.1"
[25] "3.2.1" "3.3.1" "3.3.2" "3.4.1" "3.4.2" "3.6.1" "3.7.2" "3.8.2" "3.9.2" "3.b.2" "4.1.1" "4.1.2"
[37] "4.2.1" "4.2.2" "4.3.1" "4.6.1" "4.7.1" "4.a.1" "4.b.1" "4.c.1" "5.2.1" "5.2.2" "5.3.1" "5.4.1"
[49] "5.6.1" "5.6.2" "5.a.1" "5.a.2" "5.b.1" "6.1.1" "6.3.2" "6.4.1" "6.4.2" "6.5.2" "6.6.1" "6.a.1"
[61] "6.b.1" "7.1.1" "7.2.1" "7.3.1" "7.a.1" "7.b.1" "8.1.1" "8.2.1" "8.3.1" "8.4.1" "8.4.2" "8.5.1"
[73] "8.6.1" "8.7.1" "8.8.2" "8.9.1" "8.a.1" "8.b.1" "9.2.1" "9.2.2" "9.3.1" "9.3.2" "9.4.1" "9.5.1"
[85] "9.5.2" "9.a.1" "9.b.1" "10.1.1" "10.3.1" "10.6.1" "10.7.1" "10.7.4" "10.a.1" "10.b.1" "11.1.1" "11.3.1"
[97] "11.3.2" "11.5.1" "11.5.2" "11.6.1" "11.7.2" "11.a.1" "11.b.1" "11.b.2" "12.1.1" "12.2.1" "12.4.1" "12.8.1"
[109] "13.1.2" "13.2.1" "13.2.2" "14.4.1" "14.5.1" "14.6.1" "14.7.1" "14.c.1" "15.1.1" "15.1.2" "15.4.1" "15.7.1"
[121] "15.9.1" "15.a.1" "16.1.1" "16.1.3" "16.2.1" "16.5.2" "16.6.1" "16.6.2" "16.8.1" "16.10.1" "16.10.2" "16.a.1"
[133] "17.1.1" "17.2.1" "17.4.1" "17.5.1" "17.10.1" "17.11.1" "17.15.1" "17.18.2" "17.18.3" "1.2.1" "1.b.1" "6.2.1"
[145] "11.2.1" "9.1.1" "3.8.1" "15.3.1" "16.b.1" "14.b.1" "13.1.1" "12.3.1" "13.1.3" "17.12.1" "17.9.1" "17.1.2"
[157] "11.4.1" "13.3.1" "12.c.1" "16.2.3" "15.6.1" "3.2.2" "3.3.3" "3.9.1" "3.9.3" "8.8.1" "5.3.2" "16.9.1"
[169] "5.5.2" "5.c.1" "9.c.1" "17.8.1" "12.a.1" "10.2.1" "17.3.2" "8.5.2" "12.2.2" "12.b.1" "10.4.1" "10.c.1"
[181] "16.1.2" "16.4.1" "17.19.2" "12.4.2" "17.14.1" "13.b.1" "13.a.1" "15.b.1" "17.16.1" "15.2.1" "15.4.2" "15.c.1"
[193] "16.3.1" "16.3.3" "16.7.2" "17.19.1"
#nodes$goal <- stri_match_first_regex(nodes$indicator, "(.*?)\\.")[,2]
#nodes$goal <-as.numeric(nodes$goal)
nodes<-merge(x=nodes,y=indicator_info,by="Indicator",all.x=TRUE)
g<-graph_from_data_frame(edgelist, directed=FALSE, vertices=nodes)
in_degree<-degree(g, mode="in")
in_degree<-as.data.frame(in_degree)
in_degree <- cbind(rownames(in_degree), in_degree)
rownames(in_degree) <- NULL
colnames(in_degree) <- c("Indicator","in_degree")
str_replace_all(in_degree$Indicator, fixed(" "), "")
[1] "1.1.1" "1.2.1" "1.2.2" "1.3.1" "1.4.1" "1.4.2" "1.5.1" "1.5.2" "1.5.3" "1.5.4" "1.a.1" "1.a.2"
[13] "1.b.1" "10.1.1" "10.2.1" "10.3.1" "10.4.1" "10.6.1" "10.7.1" "10.7.4" "10.a.1" "10.b.1" "10.c.1" "11.1.1"
[25] "11.2.1" "11.3.1" "11.3.2" "11.4.1" "11.5.1" "11.5.2" "11.6.1" "11.7.2" "11.a.1" "11.b.1" "11.b.2" "12.1.1"
[37] "12.2.1" "12.2.2" "12.3.1" "12.4.1" "12.4.2" "12.8.1" "12.a.1" "12.b.1" "12.c.1" "13.1.1" "13.1.2" "13.1.3"
[49] "13.2.1" "13.2.2" "13.3.1" "13.a.1" "13.b.1" "14.4.1" "14.5.1" "14.6.1" "14.7.1" "14.b.1" "14.c.1" "15.1.1"
[61] "15.1.2" "15.2.1" "15.3.1" "15.4.1" "15.4.2" "15.6.1" "15.7.1" "15.9.1" "15.a.1" "15.b.1" "15.c.1" "16.1.1"
[73] "16.1.2" "16.1.3" "16.10.1" "16.10.2" "16.2.1" "16.2.3" "16.3.1" "16.3.3" "16.4.1" "16.5.2" "16.6.1" "16.6.2"
[85] "16.7.2" "16.8.1" "16.9.1" "16.a.1" "16.b.1" "17.1.1" "17.1.2" "17.10.1" "17.11.1" "17.12.1" "17.14.1" "17.15.1"
[97] "17.16.1" "17.18.2" "17.18.3" "17.19.1" "17.19.2" "17.2.1" "17.3.2" "17.4.1" "17.5.1" "17.8.1" "17.9.1" "2.2.1"
[109] "2.2.2" "2.2.3" "2.3.1" "2.3.2" "2.4.1" "2.5.1" "2.5.2" "2.a.1" "2.a.2" "2.b.1" "2.c.1" "3.1.1"
[121] "3.2.1" "3.2.2" "3.3.1" "3.3.2" "3.3.3" "3.4.1" "3.4.2" "3.6.1" "3.7.2" "3.8.1" "3.8.2" "3.9.1"
[133] "3.9.2" "3.9.3" "3.b.2" "4.1.1" "4.1.2" "4.2.1" "4.2.2" "4.3.1" "4.6.1" "4.7.1" "4.a.1" "4.b.1"
[145] "4.c.1" "5.2.1" "5.2.2" "5.3.1" "5.3.2" "5.4.1" "5.5.2" "5.6.1" "5.6.2" "5.a.1" "5.a.2" "5.b.1"
[157] "5.c.1" "6.1.1" "6.2.1" "6.3.2" "6.4.1" "6.4.2" "6.5.2" "6.6.1" "6.a.1" "6.b.1" "7.1.1" "7.2.1"
[169] "7.3.1" "7.a.1" "7.b.1" "8.1.1" "8.2.1" "8.3.1" "8.4.1" "8.4.2" "8.5.1" "8.5.2" "8.6.1" "8.7.1"
[181] "8.8.1" "8.8.2" "8.9.1" "8.a.1" "8.b.1" "9.1.1" "9.2.1" "9.2.2" "9.3.1" "9.3.2" "9.4.1" "9.5.1"
[193] "9.5.2" "9.a.1" "9.b.1" "9.c.1"
nodes<-merge(x=nodes,y=in_degree,by="Indicator",all.x=TRUE)
nodes<-nodes %>%
select(Indicator, Goal, Indicator_title, in_degree)
nodes
Visualization
In the network graph below, the size of each vertices (each indicator) represents the number of related indicators that are connected to it. The width of the edges linking each indicator is determined according to the similarity score between each pair of related indicators. The indicators are grouped according to the goals they belong to, which are denoted by different colors of the vertices.
edges <- edgelist %>% dplyr::rename(Indicator = indicator)
nodes <- data.frame(id = nodes$Indicator,
label = nodes$Indicator,
group = nodes$Goal,
color = ifelse(nodes$Goal == 1,"#ea1d2d",ifelse(nodes$Goal == 2,"#d19f2a",ifelse(nodes$Goal == 3,"#2d9a47",
ifelse(nodes$Goal == 4,"#c22033",ifelse(nodes$Goal == 5,"#ef412a",ifelse(nodes$Goal == 6,"#00add8",
ifelse(nodes$Goal == 7,"#fdb714",ifelse(nodes$Goal == 8,"#8f1838",ifelse(nodes$Goal == 9,"#f36e24",
ifelse(nodes$Goal == 10,"#e01a83",ifelse(nodes$Goal == 11,"#f99d25",ifelse(nodes$Goal == 12,"#cd8b2a",
ifelse(nodes$Goal == 13,"#48773c",ifelse(nodes$Goal == 14,"#007dbb",ifelse(nodes$Goal == 15,"#40ae49",
ifelse(nodes$Goal == 16,"#00558a","#1a3668")))))))))))))))),
highlight = ifelse(nodes$Goal == 1,"#ea1d2d",ifelse(nodes$Goal == 2,"#d19f2a",ifelse(nodes$Goal == 3,"#2d9a47",
ifelse(nodes$Goal == 4,"#c22033",ifelse(nodes$Goal == 5,"#ef412a",ifelse(nodes$Goal == 6,"#00add8",
ifelse(nodes$Goal == 7,"#fdb714",ifelse(nodes$Goal == 8,"#8f1838",ifelse(nodes$Goal == 9,"#f36e24",
ifelse(nodes$Goal == 10,"#e01a83",ifelse(nodes$Goal == 11,"#f99d25",ifelse(nodes$Goal == 12,"#cd8b2a",
ifelse(nodes$Goal == 13,"#48773c",ifelse(nodes$Goal == 14,"#007dbb",ifelse(nodes$Goal == 15,"#40ae49",
ifelse(nodes$Goal == 16,"#00558a","#1a3668")))))))))))))))),
size = nodes$in_degree*10)
edges <- data.frame(from = edges$Indicator, to=edges$related_indicator, width = edges$similarity_score*4, color='gray')
nodes$shape <- "dot"
nodes$shadow <- FALSE
# this section doesn't allow our graph to show up - no idea why.
# nodes$color.background <- nodes$color
# nodes$color.border <- nodes$color
# nodes$color.highlight.background <- nodes$color
# nodes$color.highlight.border <- nodes$color
edges$color <- "gray" # line color
edges$smooth <- FALSE # should the edges be curved?
edges$shadow <- FALSE
visnet<-visNetwork(nodes,edges, height = "700px", width = "100%", main="Text Network Model",submain= "UN SDG Indicator Metadata", footer="Zoom in to see indicator name, click/hover to see indicator title") %>%
visEdges(smooth = FALSE) %>%
visOptions(selectedBy = "Goal",
highlightNearest = TRUE,
nodesIdSelection = TRUE) #%>%
#visLegend(main="Legend",position="right", ncol=1)
visnet
visSave(visnet, file = "Text Network Model.html")
Network visualization using output from the social network model
Indonesia
###Data preparation
edgelistindo <- read.csv("~/Documents/GitHub/G5055_Practicum_Project2/Data/PCA_results/indo_coefficients_sig.csv")
#Some preprocessing
edgelistindo<-edgelistindo%>%
select(Var1, Var2, value)%>%
filter(Var1!=Var2)
names(edgelistindo)<-c("from","to","value")
edgelistindo
For future classification of indicators into the goals they belong to, create the nodes dataframe:
indonodes <- edgelistindo %>%
select(from, to)
indonodes <- data.frame(Indicator = unlist(indonodes, use.names = FALSE))
indonodes <- distinct(indonodes)
#indonodes$goal <- stri_match_first_regex(indonodes$indicator, "(.*?)\\.")[,2]
#indonodes$goal <-as.numeric(indonodes$goal)
indonodes<-merge(x=indonodes,y=indicator_info,by="Indicator",all.x=TRUE)
g2<-graph_from_data_frame(edgelistindo, directed=FALSE, vertices=indonodes)
in_degree<-degree(g2, mode="in")
in_degree<-as.data.frame(in_degree)
in_degree <- cbind(rownames(in_degree), in_degree)
rownames(in_degree) <- NULL
colnames(in_degree) <- c("Indicator","in_degree")
indonodes<-merge(x=indonodes,y=in_degree,by="Indicator",all.x=TRUE)
indonodes<-indonodes %>%
arrange(Goal)
indonodes<-indonodes %>%
select(Indicator, Goal, Indicator_title, in_degree)
indonodes
indonodes
Visualization
indicator_info <- read.csv("../../../Data/Text_Model_Data/indicator_att.csv")
library(stringr)
str_replace_all(indicator_info$Indicator, fixed(" "), "")
[1] "1.1.1" "1.2.1" "1.2.2" "1.3.1" "1.4.1" "1.4.2" "1.5.1" "1.5.2" "1.5.3" "1.5.4" "1.a.1" "1.a.2"
[13] "1.b.1" "2.1.1" "2.1.2" "2.2.1" "2.2.2" "2.2.3" "2.3.1" "2.3.2" "2.4.1" "2.5.1" "2.5.2" "2.a.1"
[25] "2.a.2" "2.b.1" "2.c.1" "3.1.1" "3.1.2" "3.2.1" "3.2.2" "3.3.1" "3.3.2" "3.3.3" "3.3.4" "3.3.5"
[37] "3.4.1" "3.4.2" "3.5.1" "3.5.2" "3.6.1" "3.7.1" "3.7.2" "3.8.1" "3.8.2" "3.9.1" "3.9.2" "3.9.3"
[49] "3.a.1" "3.b.1" "3.b.2" "3.b.3" "3.c.1" "3.d.1" "3.d.2" "4.1.1" "4.1.2" "4.2.1" "4.2.2" "4.3.1"
[61] "4.4.1" "4.5.1" "4.6.1" "4.7.1" "4.a.1" "4.b.1" "4.c.1" "5.1.1" "5.2.1" "5.2.2" "5.3.1" "5.3.2"
[73] "5.4.1" "5.5.1" "5.5.2" "5.6.1" "5.6.2" "5.a.1" "5.a.2" "5.b.1" "5.c.1" "6.1.1" "6.2.1" "6.3.1"
[85] "6.3.2" "6.4.1" "6.4.2" "6.5.1" "6.5.2" "6.6.1" "6.a.1" "6.b.1" "7.1.1" "7.1.2" "7.2.1" "7.3.1"
[97] "7.a.1" "7.b.1" "8.1.1" "8.2.1" "8.3.1" "8.4.1" "8.4.2" "8.5.1" "8.5.2" "8.6.1" "8.7.1" "8.8.1"
[109] "8.8.2" "8.9.1" "8.10.1" "8.10.2" "8.a.1" "8.b.1" "9.1.1" "9.1.2" "9.2.1" "9.2.2" "9.3.1" "9.3.2"
[121] "9.4.1" "9.5.1" "9.5.2" "9.a.1" "9.b.1" "9.c.1" "10.1.1" "10.2.1" "10.3.1" "10.4.1" "10.4.2" "10.5.1"
[133] "10.6.1" "10.7.1" "10.7.2" "10.7.3" "10.7.4" "10.a.1" "10.b.1" "10.c.1" "11.1.1" "11.2.1" "11.3.1" "11.3.2"
[145] "11.4.1" "11.5.1" "11.5.2" "11.6.1" "11.6.2" "11.7.1" "11.7.2" "11.a.1" "11.b.1" "11.b.2" "12.1.1" "12.2.1"
[157] "12.2.2" "12.3.1" "12.4.1" "12.4.2" "12.5.1" "12.6.1" "12.7.1" "12.8.1" "12.a.1" "12.b.1" "12.c.1" "13.1.1"
[169] "13.1.2" "13.1.3" "13.2.1" "13.2.2" "13.3.1" "13.a.1" "13.b.1" "14.1.1" "14.2.1" "14.3.1" "14.4.1" "14.5.1"
[181] "14.6.1" "14.7.1" "14.a.1" "14.b.1" "14.c.1" "15.1.1" "15.1.2" "15.2.1" "15.3.1" "15.4.1" "15.4.2" "15.5.1"
[193] "15.6.1" "15.7.1" "15.8.1" "15.9.1" "15.a.1" "15.b.1" "15.c.1" "16.1.1" "16.1.2" "16.1.3" "16.1.4" "16.2.1"
[205] "16.2.2" "16.2.3" "16.3.1" "16.3.2" "16.3.3" "16.4.1" "16.4.2" "16.5.1" "16.5.2" "16.6.1" "16.6.2" "16.7.1"
[217] "16.7.2" "16.8.1" "16.9.1" "16.10.1" "16.10.2" "16.a.1" "16.b.1" "17.1.1" "17.1.2" "17.2.1" "17.3.1" "17.3.2"
[229] "17.4.1" "17.5.1" "17.6.1" "17.7.1" "17.8.1" "17.9.1" "17.10.1" "17.11.1" "17.12.1" "17.13.1" "17.14.1" "17.15.1"
[241] "17.16.1" "17.17.1" "17.18.2" "17.18.3" "17.19.1" "17.19.2"
Textdata <- datatable(indicator_info, rownames=TRUE, caption=htmltools::tags$caption(style="caption-side: bottom; text-align: center;", "Innovative counties in the U.S."), filter="top", extensions="Buttons", options=list(dom = "Bfrtip", buttons = c("colvis", "copy", "csv", "excel", "pdf", "print")))
Textdata
Guatemala (Not finished)
Data preparation
edgelistguate <- read.csv("~/Documents/GitHub/G5055_Practicum_Project2/Data/PCA_results/gua_coefficients_sig.csv")
#Some preprocessing
edgelistguate<-edgelistguate%>%
select(Var1, Var2, value)%>%
filter(Var1!=Var2)
names(edgelistindo)<-c("from","to","value")
edgelistguate
Visualization
guatenodes <- edgelistguate %>%
select(Var1, Var2)
guatenodes <- data.frame(indicatorname = unlist(guatenodes, use.names = FALSE))
guatenodes <- distinct(guatenodes)
#guatenodes$goal <- stri_match_first_regex(guatenodes$indicator, "(.*?)\\.")[,2]
#guatenodes$goal <-as.numeric(guatenodes$goal)
g3<-graph_from_data_frame(edgelistguate, directed=FALSE, vertices=guatenodes)
guatenodes
---
title: "R Notebook"
output: html_notebook
---

---
title: "Interactive Networks"
author: "Li Peishan"
date: "11/23/2021"
output:
  html_notebook:
    toc: yes
    theme: journal
---
<style>
body{ /* Normal */
font-size: 15px;
color: black;
}
write {  
line-height: 7em;
}
table { /* Table */
font-size: 12px;
}
h1 { /* Header 1 */
font-size: 30px;
}
h2 { /* Header 2 *
font-size: 26px;
}
h3 { /* Header 3 */
font-size: 22px;
}
code.r{ /* Code block */
font-size: 14px;
}
pre { /* Code block */
font-size: 14px
}
.main-container {
    width: 80%;
    max-width: unset;
}
</style>

```{r setup, echo=FALSE, eval=TRUE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE,eval=TRUE, message=FALSE, warning=FALSE)
```

```{r load packages, echo=FALSE, eval=TRUE}
library(readxl)
library(magrittr)
library(dplyr)
library(ggplot2)    
library(ggthemes)
library(tidyverse)
library(jsonlite)
library(tidyr)
library(tidytext)
library(textdata)
library(tm)
library(quanteda)
library(rvest)
library(stringr)
library(SnowballC)
library(wordcloud)
library(plotrix)
library(qdapDictionaries)
library(formattable)
library(stringr)
library(DT)
library(network)
library(ggnetwork)
library(igraph)
library(RColorBrewer)
library(randomcoloR)
library(stringi)
library(igraph)
library(ggraph)
library(graphlayouts)
library(visNetwork)
```
# Network visualization using output from text model
## Data preparation
```{r import data, echo=TRUE, eval=TRUE}
edgelist <- read.csv("../../../Data/Text_Model_Data/edgelist.csv")
edgelist
```
Indicator title
```{r, text titles, echo=TRUE, eval=TRUE}
indicator_info <- read.csv("../../../Data/Text_Model_Data/indicator_att.csv")
library(stringr)
str_replace_all(indicator_info$Indicator, fixed(" "), "")
Textdata <- datatable(indicator_info, rownames=TRUE, caption=htmltools::tags$caption(style="caption-side: bottom; text-align: center;", "Innovative counties in the U.S."), filter="top", extensions="Buttons", options=list(dom = "Bfrtip", buttons = c("colvis", "copy", "csv", "excel", "pdf", "print")))
Textdata
```
For future classification of indicators into the goals they belong to, create the nodes dataframe:
```{r create nodes, echo=TRUE, eval=TRUE}
nodes <- edgelist %>%
  select(indicator, related_indicator)
nodes <- data.frame(Indicator = unlist(nodes, use.names = FALSE))
nodes <- distinct(nodes)
str_replace_all(nodes$Indicator, fixed(" "), "")
#nodes$goal <- stri_match_first_regex(nodes$indicator, "(.*?)\\.")[,2]
#nodes$goal <-as.numeric(nodes$goal)
nodes<-merge(x=nodes,y=indicator_info,by="Indicator",all.x=TRUE)
g<-graph_from_data_frame(edgelist, directed=FALSE, vertices=nodes)
in_degree<-degree(g, mode="in")
in_degree<-as.data.frame(in_degree)
in_degree <- cbind(rownames(in_degree), in_degree)
rownames(in_degree) <- NULL
colnames(in_degree) <- c("Indicator","in_degree")
str_replace_all(in_degree$Indicator, fixed(" "), "")
nodes<-merge(x=nodes,y=in_degree,by="Indicator",all.x=TRUE)
nodes<-nodes %>%
  select(Indicator, Goal, Indicator_title, in_degree)
nodes
```
## Visualization
In the network graph below, the size of each vertices (each indicator) represents the number of related indicators that are connected to it. The width of the edges linking each indicator is determined according to the similarity score between each pair of related indicators. The indicators are grouped according to the goals they belong to, which are denoted by different colors of the vertices.
```{r Static network from text, fig.width=15, fig.height=15, echo=FALSE, eval=FALSE}
g<-graph_from_data_frame(edgelist, directed=FALSE, vertices=nodes)
#Add attributes
E(g)$weight<-E(g)$similarity_score
V(g)$in_degree<-degree(g, mode="in")
colrs<-c("#ea1d2d", "#d19f2a","#2d9a47", "#c22033","#ef412a", "#00add9", "#fdb714", "#8f1838", "#f36e24", "#e01a83", "#f99d25", "#cd8b2a", "#48773c", "#007dbb", "#40ae49",  "#00558a", "#1a3668")
V(g)$color<-colrs[V(g)$Goal]
#Plot graph
plot(g, vertex.label=NA, edge.color="gray77", vertex.color=V(g)$color, vertex.size=V(g)$in_degree, edge.width=E(g)$weight*10, layout=layout_nicely(g))
plot(g, vertex.label.color="black", vertex.label.cex=2.5, edge.color="gray77", vertex.color=V(g)$color, vertex.size=V(g)$in_degree, edge.width=E(g)$weight*10, layout=layout_nicely(g))
#legend(x=-11, y=-11, c("Goal 1","Goal 2","Goal 3","Goal 4","Goal 5","Goal 6","Goal 7","Goal 8","Goal 9","Goal 10","Goal 11", "Goal 12","Goal 13", "Goal 14", "Goal 15", "Goal 16", "Goal 17"), pch=20, col="#777777", pt.bg=colrs, pt.cex=2, cex=.8, bty="n", ncol=1)
```

```{r, Interactive Text Network Connie, echo=TRUE, eval=TRUE}

edges <- edgelist %>% dplyr::rename(Indicator = indicator)

nodes <- data.frame(id = nodes$Indicator,
                    label = nodes$Indicator,
                    group = nodes$Goal,
                    color = ifelse(nodes$Goal == 1,"#ea1d2d",ifelse(nodes$Goal == 2,"#d19f2a",ifelse(nodes$Goal == 3,"#2d9a47",
                            ifelse(nodes$Goal == 4,"#c22033",ifelse(nodes$Goal == 5,"#ef412a",ifelse(nodes$Goal == 6,"#00add8",
                            ifelse(nodes$Goal == 7,"#fdb714",ifelse(nodes$Goal == 8,"#8f1838",ifelse(nodes$Goal == 9,"#f36e24",
                            ifelse(nodes$Goal == 10,"#e01a83",ifelse(nodes$Goal == 11,"#f99d25",ifelse(nodes$Goal == 12,"#cd8b2a",
                            ifelse(nodes$Goal == 13,"#48773c",ifelse(nodes$Goal == 14,"#007dbb",ifelse(nodes$Goal == 15,"#40ae49",
                            ifelse(nodes$Goal == 16,"#00558a","#1a3668")))))))))))))))),
                    highlight = ifelse(nodes$Goal == 1,"#ea1d2d",ifelse(nodes$Goal == 2,"#d19f2a",ifelse(nodes$Goal == 3,"#2d9a47",
                            ifelse(nodes$Goal == 4,"#c22033",ifelse(nodes$Goal == 5,"#ef412a",ifelse(nodes$Goal == 6,"#00add8",
                            ifelse(nodes$Goal == 7,"#fdb714",ifelse(nodes$Goal == 8,"#8f1838",ifelse(nodes$Goal == 9,"#f36e24",
                            ifelse(nodes$Goal == 10,"#e01a83",ifelse(nodes$Goal == 11,"#f99d25",ifelse(nodes$Goal == 12,"#cd8b2a",
                            ifelse(nodes$Goal == 13,"#48773c",ifelse(nodes$Goal == 14,"#007dbb",ifelse(nodes$Goal == 15,"#40ae49",
                            ifelse(nodes$Goal == 16,"#00558a","#1a3668")))))))))))))))),
                    size = nodes$in_degree*10)

edges <- data.frame(from = edges$Indicator, to=edges$related_indicator, width = edges$similarity_score*4, color='gray')

nodes$shape  <- "dot"  
nodes$shadow <- FALSE

# this section doesn't allow our graph to show up - no idea why. 
# nodes$color.background <- nodes$color 
# nodes$color.border <- nodes$color 
# nodes$color.highlight.background <- nodes$color 
# nodes$color.highlight.border <- nodes$color 


edges$color <- "gray"    # line color  
edges$smooth <- FALSE    # should the edges be curved?
edges$shadow <- FALSE

visnet<-visNetwork(nodes,edges, height = "700px", width = "100%", main="Text Network Model",submain= "UN SDG Indicator Metadata", footer="Zoom in to see indicator name, click/hover to see indicator title") %>%
    visEdges(smooth = FALSE) %>%

  visOptions(selectedBy = "Goal", 
             highlightNearest = TRUE, 
             nodesIdSelection = TRUE) #%>%
  #visLegend(main="Legend",position="right", ncol=1)
visnet
visSave(visnet, file = "Text Network Model.html")
```
# Network visualization using output from the social network model
## Indonesia
###Data preparation
```{r import Indonesia network coefficients, echo=TRUE, eval=TRUE}
edgelistindo <- read.csv("~/Documents/GitHub/G5055_Practicum_Project2/Data/PCA_results/indo_coefficients_sig.csv")
#Some preprocessing
edgelistindo<-edgelistindo%>%
  select(Var1, Var2, value)%>%
  filter(Var1!=Var2)
names(edgelistindo)<-c("from","to","value")
edgelistindo
```
For future classification of indicators into the goals they belong to, create the nodes dataframe:
```{r Indonesia nodes, echo=TRUE, eval=TRUE}
indonodes <- edgelistindo %>%
  select(from, to)
indonodes <- data.frame(Indicator = unlist(indonodes, use.names = FALSE))
indonodes <- distinct(indonodes)
#indonodes$goal <- stri_match_first_regex(indonodes$indicator, "(.*?)\\.")[,2]
#indonodes$goal <-as.numeric(indonodes$goal)
indonodes<-merge(x=indonodes,y=indicator_info,by="Indicator",all.x=TRUE)
g2<-graph_from_data_frame(edgelistindo, directed=FALSE, vertices=indonodes)
in_degree<-degree(g2, mode="in")
in_degree<-as.data.frame(in_degree)
in_degree <- cbind(rownames(in_degree), in_degree)
rownames(in_degree) <- NULL
colnames(in_degree) <- c("Indicator","in_degree")
indonodes<-merge(x=indonodes,y=in_degree,by="Indicator",all.x=TRUE)
indonodes<-indonodes %>%
  arrange(Goal)
indonodes<-indonodes %>%
  select(Indicator, Goal, Indicator_title, in_degree)
indonodes
indonodes
```
### Visualization
```{r, echo=TRUE, eval=TRUE, fig.height=10, fig.width=10}
vis.nodes <- indonodes
vis.links <- edgelistindo
vis.nodes$shape  <- "dot"  
vis.nodes$shadow <- FALSE # Nodes will drop shadow
vis.nodes$title  <- vis.nodes$Indicator_title # Text on click
vis.nodes$label  <- vis.nodes$Indicator # Node label
vis.nodes$size   <- vis.nodes$in_degree # Node size
#vis.nodes$group <- vis.nodes$Goal
vis.nodes$color.background <- c("#ea1d2d", "#d19f2a","#2d9a47", "#c22033","#ef412a", "#00add9", "#fdb714", "#8f1838", "#f36e24", "#e01a83", "#f99d25", "#cd8b2a", "#48773c", "#007dbb", "#40ae49",  "#00558a", "#1a3668")[vis.nodes$Goal]
vis.nodes$color.border <- c("#ea1d2d", "#d19f2a","#2d9a47", "#c22033","#ef412a", "#00add9", "#fdb714", "#8f1838", "#f36e24", "#e01a83", "#f99d25", "#cd8b2a", "#48773c", "#007dbb", "#40ae49",  "#00558a", "#1a3668")[vis.nodes$Goal]
vis.nodes$color.highlight.background <- c("#ea1d2d", "#d19f2a","#2d9a47", "#c22033","#ef412a", "#00add9", "#fdb714", "#8f1838", "#f36e24", "#e01a83", "#f99d25", "#cd8b2a", "#48773c", "#007dbb", "#40ae49",  "#00558a", "#1a3668")[vis.nodes$Goal]
vis.nodes$color.highlight.border <- c("#ea1d2d", "#d19f2a","#2d9a47", "#c22033","#ef412a", "#00add9", "#fdb714", "#8f1838", "#f36e24", "#e01a83", "#f99d25", "#cd8b2a", "#48773c", "#007dbb", "#40ae49",  "#00558a", "#1a3668")[vis.nodes$Goal]
vis.links$width <- vis.links$value*100 # line width
vis.links$color <- "gray"    # line color  
#vis.links$arrows <- "middle" # arrows: 'from', 'to', or 'middle'
vis.links$smooth <- FALSE    # should the edges be curved?
vis.links$shadow <- FALSE 
visnet<-visNetwork(vis.nodes,vis.links, height = "700px", width = "100%", main="Social Network Model-Indonesia", submain="UN SDG Indicator Database",footer= "Zoom in to see indicator name, click or hover to see indicator title") %>%
  visOptions(selectedBy = "Goal", 
             highlightNearest = TRUE, 
             nodesIdSelection = TRUE) #%>%
  #visLegend(main="Legend", position="right", ncol=1)
visnet
visSave(visnet, file = "Social Network Model-Indonesia.html")
```

## Guatemala (Not finished)
### Data preparation
```{r import Guatemala network coefficients, echo=TRUE, eval=TRUE}
edgelistguate <- read.csv("~/Documents/GitHub/G5055_Practicum_Project2/Data/PCA_results/gua_coefficients_sig.csv")
#Some preprocessing
edgelistguate<-edgelistguate%>%
  select(Var1, Var2, value)%>%
  filter(Var1!=Var2)
names(edgelistindo)<-c("from","to","value")
edgelistguate
```
### Visualization
```{r Guatemala nodes, echo=TRUE, eval=TRUE}
guatenodes <- edgelistguate %>%
  select(Var1, Var2)
guatenodes <- data.frame(indicatorname = unlist(guatenodes, use.names = FALSE))
guatenodes <- distinct(guatenodes)
#guatenodes$goal <- stri_match_first_regex(guatenodes$indicator, "(.*?)\\.")[,2]
#guatenodes$goal <-as.numeric(guatenodes$goal)
g3<-graph_from_data_frame(edgelistguate, directed=FALSE, vertices=guatenodes)
guatenodes
```
